This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. The goal is drive a car in simulator and in real world test ground. Car must be able to drive through pre recorded track and stop on red traffic lights. For Traffic lights detection existing SSD was trained on Bosch small traffic lights dataset. For classification simple CNN was trained on handcrafted dataset. For car controlling was used simple PID.
Anton Pechenko forpost78@gmail.com
Kai Detlefsen kai.detlefsen@gmx.de
Andrew Ayers keeper63@cox.net
Muddassir Ahmed Khan 13beemahmed@seecs.edu.pk
Ng Fang Kiang kiang.ng@hotmail.com
Install Docker-CE from here.
# on Ubuntu 16.04 prefer version 17.06.0~ce-0~ubuntu instead 17.06.0~ce-1~ubuntu
# because of the issue https://github.com/moby/moby/issues/22207
# or docker may eat all of your disk space
sudo apt-get install docker-ce=17.06.0~ce-0~ubuntu
# add yourself to the docker group and relogin
sudo usermod -a -G docker username
If you have Nvidia GPU install Nvidia-Docker from here
# clone this repository
git clone https://github.com/parilo/CarND-Capstone.git
cd CarND-Capstone
# download
# http://parilo.ru/ssd/model.ckpt-226812.data-00000-of-00001
# into
# ros/src/tl_detector/tl_ssd_detector/checkpoint/
# Run and enter the docker container
# run GPU version of the container
nvidia-docker run -dit --name udacity-carnd-ros -v "$(pwd)":/home/ros/project --user "$(id -u)" -p 4567:4567 -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix parilo/udacity-carnd-ros-gpu:ver1 bash
# enter the container
nvidia-docker exec -it udacity-carnd-ros bash
# run none-GPU version
docker run -dit --name udacity-carnd-ros -v "$(pwd)":/home/ros/project --user "$(id -u)" -p 4567:4567 -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix parilo/udacity-carnd-ros-nogpu:ver1 bash
# enter the container
docker exec -it udacity-carnd-ros bash
# run this on your host OS in order to be able to run GUI apps
xhost +
# you may safely leave the container with exit command and reenter it again
# compile and run the code
cd /home/ros/project/ros
source /opt/ros/kinetic/setup.bash
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
# then run simualtor
# remember that all your modifications of the system in the container
# will be lost after container removed
# all changes in /home/ros/project will be preserved
# stop and remove docker container
docker container stop udacity-carnd-ros
docker container rm udacity-carnd-ros
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Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.
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If using a Virtual Machine to install Ubuntu, use the following configuration as minimum:
- 2 CPU
- 2 GB system memory
- 25 GB of free hard drive space
The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this.
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Follow these instructions to install ROS
- ROS Kinetic if you have Ubuntu 16.04.
- ROS Indigo if you have Ubuntu 14.04.
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- Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)
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Download the Udacity Simulator.
- Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git- Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt- Make and run styx
cd ros
source /opt/ros/kinetic/setup.bash
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch- Run the simulator
- Download training bag that was recorded on the Udacity self-driving car
- Unzip the file
unzip traffic_light_bag_files.zip- Play the bag file
rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag- Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch